Nova Premier 1.0 vs GPT-3.5 Turbo 16k
GPT-3.5 Turbo 16k costs 3.1x less per output token than Nova Premier 1.0 ($4.00 vs $12.50 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $12.50
- Coding
- No data
- Overall
- No data
- Output $/M
- $4.00
Nova Premier 1.0 or GPT-3.5 Turbo 16k: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Nova Premier 1.0 for a larger context window (1M against 16K tokens).
No benchmark has tested both yet. Nova Premier 1.0 has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and ALE-Bench; GPT-3.5 Turbo 16k has none yet. Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Nova Premier 1.0 has a AA Intelligence Index result, 9.2.
Directional only
Web design
Design Arena: websites
No call
Only Nova Premier 1.0 has a Design Arena: websites result, 835.
Directional only
Long documents
Listed context window
Nova Premier 1.0
Nova Premier 1.0 takes 1M tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-3.5 Turbo 16k
Nova Premier 1.0 $0.0088, GPT-3.5 Turbo 16k $0.0050 per turn.
List prices
At a glance
- Coding index
- Nova Premier 1.0 not benchmarked, GPT-3.5 Turbo 16k not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Nova Premier 1.0 $12.50 against GPT-3.5 Turbo 16k $4.00 per million output tokens, 3.1x apart.
- Context window
- Nova Premier 1.0 takes 1M tokens, GPT-3.5 Turbo 16k 16K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards measured
| Benchmark | Nova Premier 1.0 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 31.7% | Not measured | Only Nova Premier 1.0 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 6.8% | Not measured | Only Nova Premier 1.0 measured |
| ALE-Bench | 147.4 | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 40 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Nova Premier 1.0 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.2 | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Nova Premier 1.0 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 38.3% | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Nova Premier 1.0 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 56.9% | Not measured | Only Nova Premier 1.0 measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | Not measured | Only Nova Premier 1.0 measured |
| Kagi LLM Benchmark | 44.8% | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Nova Premier 1.0 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| AA Math Index | 17.3 | Not measured | Only Nova Premier 1.0 measured |
| AIME 2025 (Artificial Analysis run) | 17.3% | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Nova Premier 1.0 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 73.3% | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 19 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Nova Premier 1.0 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| Design Arena: websites | 835 | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 8 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Nova Premier 1.0 | GPT-3.5 Turbo 16k | Leader |
|---|---|---|---|
| IFBench | 36.2% | Not measured | Only Nova Premier 1.0 measured |
| Long context reasoning (AA-LCR) | 34.3% | Not measured | Only Nova Premier 1.0 measured |
| Neither model has a result on the other 46 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GPT-3.5 Turbo 16k costs less
Repository review
50K input + 3K output tokens
GPT-3.5 Turbo 16k costs less
Agent loop
200K input + 10K output tokens
Nova Premier 1.0 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Amazon Nova Premier 1.0 | OpenAI GPT-3.5 Turbo 16k |
|---|---|---|
| Maker | Amazon | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | October 31, 2025 | August 28, 2023 |
| Context window | 1M | 16K |
| Input $/Mtok | $2.50 | $3.00 |
| Output $/Mtok | $12.50 | $4.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Nova Premier 1.0 better than GPT-3.5 Turbo 16k for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Nova Premier 1.0 for a larger context window (1M against 16K tokens).
Which is cheaper, Nova Premier 1.0 or GPT-3.5 Turbo 16k?
Nova Premier 1.0 costs $2.50 per million input tokens and $12.50 per million output tokens; GPT-3.5 Turbo 16k costs $3.00 and $4.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Nova Premier 1.0 or GPT-3.5 Turbo 16k?
Nova Premier 1.0, with 1M tokens against 16K.
Has any benchmark tested both Nova Premier 1.0 and GPT-3.5 Turbo 16k?
No benchmark has tested both yet. Nova Premier 1.0 has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and ALE-Bench; GPT-3.5 Turbo 16k has none yet. Compare them on price and context instead. Data as of October 22, 2026.